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The COVID-19 pandemic has triggered the public interest in science, and thus the need to report on scientific facts and information correctly has increased. While the pandemic has posed challenges to working journalists across the globe, problems for Pakistani journalists continue to grow. The country's journalists struggle not only to report on the pandemic but also to report on other scientific matters ranging from environmental issues to technological developments. This indicates the timely need for journalists' expert knowledge to report on science. Nevertheless, science journalism research is in its nascent stage in Pakistan, and there is no prior national study that analyzes science reporting in relation to knowledge-based journalism. Therefore, informed by the conceptual model of knowledge-based journalism, this study contributes a qualitative analysis of challenges that hinder the practice of knowledge-based journalism for science reporting within the context of Pakistan. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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The indicator of bankruptcy exposure for airport operations in Malaysia is calculated by using Altman's Z'-score. Financial and non-financial attributes related to the bankruptcy exposure show multicollinearity, and the redundant information was identified and removed. The common period for the variables is from 1999-2021, which includes the period of COVID-19 pandemic. Models with a combination of financial and non-financial attributes further reduce the deviation between the estimated standard deviation of the residuals and the marginal standard deviation of the bankruptcy risk in comparison to models without the combination. The best model provides improvements in terms of the mean of the absolute errors (MAE), mean of absolute percentage errors (MAPE), and mean absolute scaled errors (MASE). Furthermore, all determinants in the best model are statistically significant. We suggest that the opportunity for optimisation, including total movements of passenger, cargo and mail, could reduce the company's bankruptcy exposure. Findings indicate that reducing the financial leverage could improve the financial distress risk while liquidity, net operating margin, and asset turnover are positively contributed to the financial stability of the largest airport operator in Malaysia. If the marginal average of annual exposures to bankruptcy of 4.04% continues linearly into the future, the company is expected to transition from being financially stable to experiencing financial distress in 2030. © 2022 IEEE.
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Authorities have suggested emergency remote instruction to guarantee that students are not left idle during the pandemic due to the sudden closing of educational facilities. Then for the time being, traditional methods (face-to-face) have been replaced by Open and Distance Learning (ODL). Face-to-face learning was preferred by the majority of students over online learning since students were not able transit to online learning and lacked inspiration. Hence, this study focuses on perception towards ODL during COVID-19 among statistics' students at FSKM UiTM Shah Alam based on some impeding factors such as social issue, lecturer issue, accessibility issue, academic issue, generic skills and learner intentions. The aim of this study is to investigate the perception of statistics' students on ODL based on impeding factors and to identify the significant impeding factors effect on statistics students' perception on ODL. There are 160 observations that are used in this study. The methods that are being used in this study are descriptive analysis and logistic regression. Overall, from the result obtained, students' perception on ODL are approximately to agree for social issue, academic issue and learner intentions variables. Meanwhile, the significance impeding factors in this study are social issue and learner intentions. This study may help higher education institution to improve and make a better strategy to improve the existing teaching method that have been applied by all lecturers. © 2022 IEEE.
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The study uses wavelet power spectrum and wavelet coherence transformation methodologies to examine how geopolitical risk affected the returns on stocks, oil, and gold during the GFC, COVID-19, and Russia-Ukraine war-three disruptive events that affected the world's financial markets. For better diversification benefits during the turbulent times, we further investigate the degree of co-movement in frequency and time domains. We observe that GPR has high variations during Russia-Ukraine war period compared to COVID-19 period and is shown to have least variation during the GFC period. WTI crude oil and DJGI indexes are observed to have high variations during GFC, and COVID-19 periods followed by Russia-Ukraine war. We further observe that GOLD offers better diversification opportunity as well as leading movement against WTI and DJGI during disruptive events in financial markets. The results provide new understanding of how geopolitical risk affects financial assets for international investors, fund managers, and regulators, which would further aid to find risky and safer haven possibilities during the turmoil periods. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
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Recent advances, in information and communication technology (ICT), have significantly impacted some critical sectors of societies (such as transport, health, business, and communication) across many developed and developing countries. Nevertheless, the Internet has proliferated unequally across the world, resulting in global digital inequalities. The onset of the COVID-19 pandemic led to the dependence on online education to prevent the interruption of academic progress in schools and universities worldwide. The global pandemic further worsened the situation for Pakistan, which is neither economically strong nor is the country's ICT infrastructure well-established to facilitate the successful accomplishment of virtual courses and classes. Thus, this study aimed to analyze the level of Internet access among the Pakistani universities' teachers and students, and their ICT skills as they applied to online education during the COVID-19 pandemic. The study used the qualitative method of email interviews and thematic analysis to present the study's results. This study revealed that most Pakistani students, especially those from rural and remote areas, experienced challenges because they neither had proper Internet access, nor could they use laptops and virtual learning systems. Students, who belonged to the upper and middle classes of urban areas and enrolled in private-sector universities, were not as directly affected by Pakistan's pervasive digital divide to carryout their education during the COVID-19 pandemic. © 2023 SAGE Publications.
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Methods: This serial population-based analysis included data from the CDC’s National Vital Statistics System on IBD decedents aged ≥25 years from 1/1/06 to 12/31/21. The rise in neoplasm-related non-COVID deaths and mortality rates prior to hospital arrival during the pandemic suggests that indirect effects of the pandemic, such as delayed presentation, likely exacerbated healthcare disparities and adversely impacted timely interventions and care. Subgroup Analyses of Observed COVID, Non-COVID, and Predicted Age-Standardized Mortality Rates Among IBD Decedents Stratification Group Year COVID ASMRs Non-COVID ASMRs Predicted ASMRs with 95% CI Age UC 25-64 years 2020 0.01 0.20* 0.17 [0.15-0.19] 2021 0.03 0.18 0.17 [0.15-0.19] ≥65 years 2020 0.28 2.31 2.30 [2.01-2.58] 2021 0.28 2.53 2.45 [2.04-2.86] CD 25-64 years 2020 0.02 0.46 0.42 [0.37-0.47] 2021 0.04 0.46 0.45 [0.38-0.52] ≥65 years 2020 0.25 2.95 2.79 [2.46-3.12] 2021 0.33 3.15 2.87 [2.49-3.25] Sex UC Male 2020 0.07 0.70 0.68 [0.62-0.75] 2021 0.09 0.69 0.70 [0.63-0.78] Females 2020 0.06 0.54 0.53 [0.44-0.61] 2021 0.07 0.60 0.57 [0.44-0.70] CD Males 2020 0.06 0.94 0.93 [0.83-1.04] 2021 0.11 0.99 1.01 [0.86-1.16] Females 2020 0.06 0.95 0.90 [0.78-1.02] 2021 0.09 0.99 0.94 [0.76-1.12] Race UC Hispanics 2020 0.04 0.26 0.23 [0.07-0.38] Non-Hispanic whites 2020 0.07 0.71 0.71 [0.63-0.78] Non-Hispanic blacks 2020 0.02 0.39 0.41 [0.29-0.52] CD Hispanics 2020 0.03 0.27 0.27 [0.08-0.47] Non-Hispanic whites 2020 0.07 1.15 1.12 [0.99-1.25] Non-Hispanic blacks 2020 0.06 0.75* 0.55 [0.41-0.70] * Signifies statistical significance ASMRs are per 100,000 persons.
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Nurses are front-line health professionals. This is especially so for those in the Emergency Room (ER) and during the COVID-19 pandemic, as they are the key to controlling virus transmission. The purpose of this article is to review the stress management strategies applied by nurses in emergency rooms throughout the COVID-19 pandemic. A scoping review was done by searching for related literature using databases from Science Direct, ProQuest, SAGE Journal, and BMJ. We explored the literature from a relevant point of view and assessed the quality of the research. The data were then mapped to identify the main themes found in the literature. A total of 36 articles were found from the search results and six articles were selected to be analyzed. We observed that stress on nurses is caused by an excessive burden of several factors, such as having a family or child, lifestyle changes, lack of access to personal protective equipment (PPE), lack of self-confidence, as well as feelings, and fatigue. We also observed how nurses managed stress, namely, by strengthening training, positive spiritual coping, social support, and access to mental health services. © 2022, Universitas Jenderal Soedirman. All rights reserved.
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BACKGROUND: Coronavirus disease 2019 (COVID-19) is a viral pneumonia that spreads rapidly globally and was designated a pandemic by the World Health Organization (WHO). The number of cases has exceeded 15,000,000 worldwide, and the disease carries a mortality rate of ± 4%. One of the complications of COVID-19 is the incidence of coagulopathy and thromboembolism. The coronavirus (SARS-CoV-2) activates inflammatory and thrombotic processes, and the presence of coagulopathy and abnormal coagulation parameters is among the most significant biomarkers for poor prognosis in COVID-19 patients. COVID-19-associated coagulopathy is characterized by a decreased platelet count and the presence of a cytokine storm, indicating an extreme hypercoagulable state. AIM: This study aims to determine the coagulation profile and outcomes of patients with moderate-severe COVID-19. METHODS: This study was conducted in Wahidin Sudirohusodo Hospital. Medical record data were included for all inpatients diagnosed with COVID-19 using the RT-PCR test, from January 2021 to August 2021. The Kolmogorov– Smirnov normality test, Chi-squared test, odds ratio (OR), Mann–Whitney U-test, and independent t-test were used for statistical analysis. Multivariate analysis was carried out using the multiple logistic regression – backward Wald method. p < 0.05 was taken as statistically significant. RESULTS: A total of 231 patients with confirmed COVID-19 were included in this study. The mean prothrombin time (PT), D-dimer, and fibrinogen were higher in severe COVID-19 patients than in moderate patients and had significant results. Platelet (PLT) levels were not found to be significant in moderate-severe COVID-19. The relationship between groups of coagulation marker variables was found to be significantly associated with moderate-severe COVID-19. All coagulation markers were significantly related to patient outcome (p < 0.05). The mean value of each variable was found to be higher in patients who died than in those with better outcomes. CONCLUSION: An increase in PT, activated partial thromboplastin time (APTT), and fibrinogen is associated with mortality in patients with moderate COVID-19. In patients with severe COVID-19, mortality is associated with increased PT. PT is, therefore, a coagulation marker that is significantly related to COVID-19 outcome.
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Purpose - Innovation has become a necessity for the information technology (IT) sector, especially during COVID-19 pandemic. Therefore, this study aims to investigate how knowledge sharing affects employees' innovative work behavior (IWB). Specifically, the study examined occupational self-efficacy (as mediating mechanism) and entrepreneurial leadership (as boundary condition) to encourage IWB. Design/methodology/approach - The study used social media platforms to collect data from 270 employees working in the IT sector through "google forms" on convenience basis between March and August 2021. The study applied structural equation modeling in two stages to examine the measurement model (for unidimensionality) and the structural model (for hypotheses testing). Findings - The study noted that knowledge sharing positively affects employees' IWB and occupational self-efficacy positively explains this association. In addition, employees' perception of entrepreneurial leadership strengthens the association between knowledge sharing and IWB. Research limitations/implications - The study collected data from a developing country during COVID-19 by using a cross-sectional design that may restrict causality. However, the findings suggest the management not only encourages knowledge sharing environment but also engages employees in various training that motivate them to experiment with new ideas and techniques. Originality/value - This study extends the existing literature on knowledge sharing and IWB by exploring occupational self-efficacy as mediating mechanism and entrepreneurial leadership as a boundary condition.
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The importance of Micro, Small, and Medium Enterprises (MSMEs) in economic development and their access to finance post-COVID-19 has been highlighted in this study. The availability of finance is a critical factor for MSMEs to flourish, and they are mostly severely affected by the economic recession. The purpose of this study was to ascertain how the firm-specific factors such as Location, Industry, Size, Age, Ownership, Collaterals, and Business information affect their access to finance in India. This study used a survey to collect primary data from 200 MSMEs in India. Descriptive and logistic regression analysis was used to analyze the data. Results show that firms with collateral, larger and older firms, and private limited firms are less likely to face problems in raising finance, while service firms are more likely to face problems in raising finance. The results from this study will add to the understanding of the financing problems faced by MSMEs in India. The study recommends that firm attributes are important for accessing finance and help policymakers and researchers develop new strategies and policies to support the financing of MSMEs in India. © 2022 Uddin et al.
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Purpose This study aims to investigate how abusive supervisor affects knowledge hiding (KH). Specifically, this study investigates employee silence as a mediating mechanism between abusive supervision and employees' KH. Further, psychological ownership is examined as a buffer between abusive supervision and employee silence. Design/methodology/approach KH has become a major issue for both manufacturing and service sectors. Therefore, this study collected data from 322 employees working in manufacturing and service sectors through "Google Forms" during COVID-19. The respondents were contacted through LinkedIn platform between January and July 2021. Findings This study noted that when employees working in high-power distance cultures perceive their leaders/supervisors as abusive, they avoid confrontation and engage in silent behavior, which positively affects their KH behavior. However, employees with a high level of psychological ownership are less likely to respond to their abusive supervisors through silence because such employees feel a greater sense of belongingness and prefer to benefit their organization. Research limitations/implications This study used a cross-sectional design that restricts causality. However, the findings of this study suggest management to focus on leadership style to minimize KH at the workplace. Originality/value To the best of the authors' knowledge, this study is the first to explore the underlying mechanism (employee silence) and boundary condition (psychological ownership) to explain the association between abusive supervision and KH.
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Novel coronavirus (COVID-19) is a hazardous virus. Initially, detected in China and spread worldwide, causing several deaths. Over time, there have been several variants of COVID-19, we have grouped all of them into two major categories. The categories are known to be variants of concern and variants of interest. Talking about the first of these two, it is very dangerous, and we need a system that can not only detect the disease but also classify it without physical interaction with a patient suffering from COVID-19. This paper proposes a Bag-of-Features (BoF) based deep learning framework that can detect as well as classify COVID-19 and all of its variants as well. Initially, the spatial features are extracted with deep convolutional models, while hand-crafted features have been extracted from several hand-crafted descriptors. Both spatial and hand-crafted features are combined to make a feature vector. This feature vector feeds the classifier to classify different variants in respective categories. The experimental results show that the proposed methodology outperforms all the existing methods. © 2021 IEEE.
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Novel coronavirus, known as COVID-19, is a very dangerous virus. Initially detected in China, it has since spread all over the world causing many deaths. There are several variants of COVID-19, which have been categorized into two major groups. These groups are variants of concern and variants of interest. Variants of concern are more dangerous, and there is a need to develop a system that can detect and classify COVID-19 and its variants without touching an infected person. In this paper, we propose a dual-stage-based deep learning framework to detect and classify COVID-19 and its variants. CT scans and chest X-ray images are used. Initially, the detection is done through a convolutional neural network, and then spatial features are extracted with deep convolutional models, while handcrafted features are extracted from several handcrafted descriptors. Both spatial and handcrafted features are combined to make a feature vector. This feature vector is called the vocabulary of features (VoF), as it contains spatial and handcrafted features. This feature vector is fed as an input to the classifier to classify different variants. The proposed model is evaluated based on accuracy, F1-score, specificity, sensitivity, specificity, Cohen's kappa, and classification error. The experimental results show that the proposed method outperforms all the existing state-of-the-art methods.
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The paper aims to evaluate the reaction of stock markets in BRICS countries (Brazil, Russia, India, China, and South Africa) to the outbreak of the COVID-19 pandemic. The study uses ARCH and GARCH models that use daily stock prices from January 1, 2020, to September 2, 2020. The financial market response was analyzed in two phases. The first phase analyses the financial markets' response within 30 to 60 days from the first day of confirmed cases of COVID-19. The second phase analyses the financial market response post 30 to 60 days of initial confirmed cases. The study results conclude that the share prices decreased, but in the second phase, the markets responded positively. Our results conclude that governmental support played an important role in mitigating the repercussions of the COVID-19 outbreak on stock markets in BRICS countries.
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Introduction: There are reports of changes in the numbers of stroke admissions and time intervals to receiving emergency treatments during the COVID-19 pandemic. We examined the impact of the COVID-19 pandemic on the stroke thrombolysis rate and delay to thrombolysis treatment in a regional stroke centre in London, UK. Methods: COVID-19 testing began at our hospital on 3 March 2020. Clinical data for all acute stroke admissions were routinely collected as part of a national Sentinel Stroke National Audit Programme (SSNAP) and all thrombolysis data were entered into our local thrombolysis database. We retrospectively extracted the relevant patient data for the period of March to May 2020 (COVID group) and compared to the same period in 2019 (pre-COVID group). Results: Compared with pre-COVID, there was a 17.5% fall in total stroke admissions (from 315 to 260) during COVID;but there were no significant differences in the demographics, stroke severity, proportions with known time of onset, or median onset-to-arrival time. The thrombolysis rates amongst ischemic strokes were not significantly different between the two groups (59/260=23% pre- COVID vs. 41/228=18% COVID, p=.19). For thrombolysis patients, their stroke severity and demographics were similar between the two both groups. Median onset-to-needle time was significantly longer by 22 minutes during COVID [127 (IQR 94-160) vs. 149 (IQR 110-124) minutes, p=.045];this delay to treatment was almost entirely due to a longer median onset-to-arrival time by 16 minutes during COVID (p=.029). Favorable early neurological outcomes post-thrombolysis (defined as an improvement in NIHSS by ≥4 points at 24 hours) were similar (45% vs. 46%, p=.86). Conclusion: COVID-19 pandemic had a negative impact on prehospital delays which in turn significantly increased onset-to-needle time, but without affecting the chance of a favorable early neurological outcome. Our data highlight the need to maintain public awareness of taking immediate action when stroke symptoms occur during the COVID-19 pandemic.
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This research aims to investigate the impact of falling oil prices at the beginning of 2020 on 82 industrial companies listed on the GCC stock markets. The research sample period is divided into two periods pre-COVID and during COVID covering the period starting 1st January 2020 to May 15, 2020. The research uses the Panel Least Square (PLS) method and Panel Generalized Method of Moments (GMM) with fixed and random effects in each country. The results of GMM models reveal a positive relationship between oil prices and the share prices of industrial companies in the Gulf countries, which confirms that the share prices of industrial companies in the Gulf Cooperation Council (GCC) countries have been negatively affected by the decline in oil prices with the beginning of 2020. The findings show that the highest impact of falling oil prices has been recorded in the industrial companies in the kingdom of Saudi Arabia. However, the falling of oil prices does not have a significant effect on industrial companies in the state of Qatar. The research results suggest that GCC economies have to move on the path of non-reliance on Oil and gas-driven economy. © Copyright: The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.